Online social interaction, education, and health care by analysing affect and cognitive features

ABSTRACT

A method of establishing a collaborative platform comprising performing a collaborative interactive session for a plurality of members, and analysing affect and/or cognitive features of some or all of the plurality of members, wherein some or all of the plurality of members from different human interaction platforms interact via the collaborative platform, wherein the affect comprises an experience of feeling or emotion, and wherein the cognitive features comprise features in a cognitive state, the cognitive state comprising a state of an internal mental process.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 13/668,337, filed Nov. 5, 2012, which claims priority to U.S. Provisional Application No. 61/720,405, filed on Oct. 31, 2012, entitled “The Next Generation of Virtual Live Education”; U.S. Provisional Application No. 61/719,980, filed Oct. 30, 2012, entitled “Online Social Interaction, Education, and Health Care by Analysing Affect and Cognitive Features”; U.S. Provisional Application No. 61/625,949, filed Apr. 18, 2012, entitled “SWAP The Next Generation of Virtual Communication Platform,” and U.S. Provisional Application No. 61/556,205, filed on Nov. 5, 2011, entitled “SWAP: FUTURE OF VIDEO CHATTING,” which are incorporated herein in their entirety by reference.

All U.S. patents and publications listed in this application are incorporated herein in their entirety by reference. This application is also related to the U.S. patents and publications listed in Appendix 1. These U.S. patents and publications listed in Appendix 1 are incorporated herein in their entirety by reference.

BACKGROUND

According to International Data Corporation (IDC), a global provider of market intelligence, video communications is one of the most promising industries with the potential to create a market of at least 150 million people in America alone in the next five years.

Certain video communication platforms for groups of individuals to create and share information, interact with each other through the software and generally use the software to achieve an individual or group objective are currently available. Generally these systems store the collaboration for future reference and further discussion or collaboration. However, these systems have several limitations that have been addressed herein. Also, novel solutions for these limitations are provided herein.

SUMMARY

The embodiments herein relate to a method of establishing a collaborative platform comprising performing a collaborative interactive session for a plurality of members, and analysing affect and cognitive features of some or all of the plurality of members.

In one embodiment, some or all of the plurality of members from different human interaction platforms interact via the collaborative platform,

One embodiment can further comprise displaying of targeted advertisements or notifications based on the context of the interactive collaborative session.

One embodiment can further comprise measuring effectiveness of the displaying of targeted advertisements or notifications.

One embodiment can further comprise integrating an application or a device within the collaborative interactive session.

Another embodiment relates to a computer implemented system comprising: a storage medium configured to store a collaborative interactive session data; and a processor configured to perform a collaborative interactive session for a plurality of members, wherein the system analyses affect and cognitive features of some or all of the plurality of members.

In one embodiment, some or all of the plurality of members from different human interaction platforms interact via the collaborative interactive session, wherein the different human interactions platforms comprise social media platforms.

In one embodiment, the system is further configured to display targeted advertisements or notifications based on the context of the interactive collaborative sessions.

In one embodiment, the system is further configured to measure effectiveness of the displaying of targeted advertisements or notifications.

In one embodiment, the system is further configured to integrate an application or a device within the collaborative interactive session.

In one embodiment, the system comprises a sound and/or video hub, wherein the sound and/or video hub allows any member of the plurality of the members to play a song and/or a video and simultaneously allows some or all of the plurality of members to listen and/or watch the song and/or the video played.

In one embodiment, the system comprises audio and/or video synopsis of the collaborative interactive session for the plurality of members using a sound and image-processing technology that creates a summary of an original full-length audio and/or video.

Another embodiment relates to a tangible non-transitory computer readable medium comprising computer executable instructions executable by one or more processors for establishing a collaborative platform comprising performing a collaborative interactive session for a plurality of members, and analyzing affect and cognitive features of some or all of the plurality of members.

In one embodiment, some or all of the plurality of members interact from different human interaction platforms.

One embodiment could further comprise computer executable instructions executable by one or more processors for displaying of targeted advertisements or notifications based on the context of the interactive collaborative sessions.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features can become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows an embodiment of data passing through the SWAP platform, wherein data is acquired and the multimedia segmented and analysed.

FIG. 2 shows an embodiment of chatting threads.

FIG. 3 shows an embodiment of profile appearance.

FIG. 4 shows an embodiment of the analysis of data through SWAP for ‘close friends’.

FIG. 5 shows an embodiment of e-learning showing an user interface for studying from a video lecture.

FIG. 6 shows an embodiment of a virtual classroom with e-material used as learning medium, based on study of eye movement, pupillary dilation and facial study.

FIG. 7 shows an embodiment of a user interface.

FIG. 8 shows an embodiment of a collated version of the user interface.

FIG. 9 shows an embodiment of how insurance or health care companies will acquire date through the cell phone of a client.

FIG. 10 shows an embodiment of how the media acquired by SWAP is going to be analyzed.

FIG. 11 shows an embodiment of a non-invasive patient tracking method.

FIG. 12 shows a flow diagram that delineates possible tracking mechanisms.

DETAILED DESCRIPTION

Large amount of online media that is transferred is merged providing convenience to user. This data is analysed to find out affect and cognitive state. Utilising this data a new form of social interaction platform is developed which will incorporate many features of real human interaction.

The term “affect” refers to the experience of feeling or emotion. Affect is a part of the process of an organism's interaction with stimuli. The word also includes affecting display, which is a facial, vocal, or gestural behavior that serves as an indicator of affect.

The term “cognitive state” refers to the state of exploring internal mental processes, for example, to study how people perceive, remember, think, speak, and solve problems.

SWAP is the acronym of an embodiment of a virtual communication platform system described herein. SWAP and a virtual communication platform system are used synonymously in this application.

Embodiments herein relate to SWAP, which can be a web-based application that serves as a multi-dimensional platform for peer-to-peer communication. Current video communication services such as Skype only provide basic face-to-face contact pathways—the interaction is limited to text, audio, and video. SWAP integrates collaboration with communication. It streamlines the base services of peer-to-peer text, audio and video communication with interaction on various collaborative platforms as well as with individual web-based activity. SWAP can incorporate existing streams of social media. SWAP strives to be the global leader in providing a unified collaboration platform using Internet communication media while enhancing the capabilities of virtual interaction of people from all walks of life. SWAP can provide young adults with a video communications application that integrates multiple streams of online media with virtual interaction. SWAP can provide a unified platform that allows users of any social media service, such as FACEBOOK® or GOOGLE+®, to interact on, removing the fragmentation within social media communication. This platform also combines text, audio, and video communication with collaboration in the areas of academia, music, and recreational activities such as gaming, extending the capabilities of current virtual communication.

This application can be organized into several spheres of interaction known as “globes”. Each globe can provide a base interaction for multiple users to collaborate. Our application can integrate these collaboration platforms with a video feed to enhance overall virtual interaction.

The data passing through the SWAP platform will be acquired and the multimedia will be segmented and analysed. This can be seen in FIG. 1. FIG. 1 depicts the SWAP platform that solves the problem of fragmentation and provides a seamless connection between users from separate platforms. These two separate platforms are integrated by SWAP. Interactions between User 1 and User 2 are then sent for analysis. These interactions are used in the Swap+ Profile, Elearning, and SWAP Project.

The derived information from analysis such as user emotion and mental states will be utilised in functioning of 3 major SWAP features—

1. Profiles (SWAP+)

2. Targeted Advertisement

3. Smart ELearning (addition to the chalkboard and virtual classroom globe)

SWAP+ Profiles

The way most social networking sites function, they mainly act as a great platform for data storage, sharing and communication. But they are all a far cry from true social interaction simulation in other words in no way are these anywhere near how we interact in society. Thus the profiles of SWAP+ will be a system which will be much closer to how we remember people, conversations and moreover how we forget. The large amount of data that get passed through the SWAP platform will be analyzed and this data will be used to shape the SWAP+ profiles. The way other people's SWAP+ profiles will appear to us. In this area we try to mimic the way in which we remember people. The profile's emotion feel will be the general emotion that we generally exhibit when we communicate that with that person through any form of media (video, text or speech) (obtained from analyzed data from conversations taking place). Keeping in trend with how we remember people in reality, since how a person is seen by is strongly shaped with event and experiences we share with that person. The profile of the person will bear events, having strong emotions behind them. Any sort media—like text, speech, video or pictures. Texts can be presented simply as they are, videos will we presented like snapshots with the option to be played by the user.

The SWAP+ profile can include:

1. Chatting threads (as depicted by FIG. 2)

2. Profile appearance (as depicted by FIG. 3)

3. Close friends (as depicted by FIG. 4)

1. Chatting Threads

The basic flaw which makes social interactions unrealistic is that every bit of data is remembered, unlike the case in real-life daily interactions. To replicate this communications that will be happening through SWAP+ will follow a similar pattern. The comments of the thread will slow start to deteriorate i.e. fade away. The period after which the part of the thread is completely forgotten will be a sort of threshold time, which will be close to average human being time for memory retaining. Memories having high cognitive strain or emotion attached will have much higher threshold time.

In FIG. 2, the comments of the thread will slow start to deteriorate i.e. fade away. The period after which the part of the thread is completely forgotten will be a sort of threshold time, which will be close to average human being time for memory retaining. Memories having high cognitive strain or emotion attached will have much higher threshold time. This example shows a conversation between two friends discussing “last night's party.” The initial conversation contains low emotionally attached or insignificant conversation (e.g. “Hey!” “What happened?” “Yeah?”). In the decayed conversation, however such aspects of the conversation are decayed into visually smaller bubbles. The larger bubbles include phrases associated with high cognitive strain or emotion attached. In this example, one friend tells the other “I told mom about how I lost her gold necklace.” This phrase is the largest bubbles, assuming that the friend was experiencing significant emotion—including perhaps anxiety, fear, etc.

2. Profile Appearance

In FIG. 3, the profile of SWAP+ will be dynamic in nature constantly changing reflecting the mental state of the user. The real-time mental state will be determined from the various analysis methods, which will be applied on the data passing through SWAP. Under a state of extreme emotion such as depression the user profile will be able to reflect this state. This will allow for other people to be notified of the user's emotional state and hence help him get back normalcy through communication. Through analysis ‘close friends’ can also be identified who under the above-mentioned situation will be notified. In this example, we see Abhishek Biswas' profile as seen by his girlfriend. Note: this profile is individualized. His girlfriend can see all the important conversations between them (as “decayed conversation” feature.) These conversations include highly emotional both positive and negative phrases. Also, highly emotional paused scenes from videos will appear as well as pictures that have been discussed in emotional conversations.

3. Close Friends

FIG. 4 demonstrates the analysis of data through SWAP for ‘close friends’. The analysis will allow the application to identify people with whom the user has discussions of high emotional content. A database of sorts can be created which will store people with whom the user has discussion of high emotional content such as high positive emotion content, high negative emotion content, people with whom through communication emotion changes from negative to positive. Also, people with whom there isn't communication of high emotion content, but volume and frequency of communication is very high, these people will also be identified as ‘close friends’.

Whenever the user is in a state of emotional extreme then the user's profile will be highlighted in the homepages of the ‘close friends’. In this example, the friend whose profile shows high levels of distress is the largest. The user can visually identify this friend and try to help her. The second largest picture is also a friend who is visually distressed (which is seen through emotions detected on his profile) and is therefore seen as a large image. The third largest image is the user's girlfriend's profile. Although her profile does not show high emotional context, her profile image is highlighted because of the high volume and frequency of communication.

Elearning

In virtual classroom or chalkboard feature the user may be required to go through leaning material or modules and solve problems. Based on observation of Pupil dilation the cognitive load on user's mind can be found out. If the user is under high cognitive stress for prolonged period it is indicative that the user is unable to make progress with current material or problem. Hence more comprehensive material may be provided and in case problems a hint or a different problem may be provided. Similarly the pupil study may also indicate the course and problems may not cause appreciable cognitive strain so in this case a course which is less comprehensive and problems of higher difficulty may be presented. The SWAP feature will allow people from different video communication platforms to join into a virtual classroom. This virtual class room will allow for multiple people to join at same time the course being taught will customized for each individual user. Thus student gets all the benefits of study in a classroom such discussion, debating, interactive doubt clearance, observing point of view of peers. At the same time the course is modified as peer the learning capacity and mental level of each individual student. So as the all students join the virtual classroom they all start out with the same course material and as they carry forward with class, constantly each student cognitive load level, attention, stress is being monitored. And based on this data material is modified that will enable maximum learning will be provided. Apart from pupillary dilation and video analysis of face, eye tracking will allowing monitoring the movement of the eyes hence it will be possible to see whether that user is being able to focus on the material. Using eye tracking technology, we can find the place where the user is looking at and pattern recognition can be utilized to find whether the material being presented is being read or not for example regularized movement of eyes indicate that the user is following the material presented and whereas wandering and random movement of eyes are indicative that the material is not being followed.

The virtual classroom element of SWAP will have advanced tools to simulate real class room like environment. The nature learning may be of 2 types; video lecture and course material.

FIG. 5 shows the user interface for studying from video lecture. The following features are present: a notepad where the user can take rough notes, inventory of study aids (like calculator), formula manual (for the course being studied), and access to all rough notes. The work area contains questions and problems that will be asked to the user as each sub segment of the video lecture is completed for those of who finish the problems quickly more problems will be asked and the next sub segment may start only after the minimum number of questions has been completed by everyone. The user will be able to communicate with his other peers and ask them for help or doubt clearance (similar to real class rooms). The feature will also be provided that allows for person who is communicating with user to share his work sheet and help in solving and understanding the problem. As can be seen in this example, the lecture is synchronized with the notes in the formula manual being displayed. Also, based on eye movement, pupillary dilation and facial study of other peers, the student (or teacher) can detect the amount of difficulty or ease his/her peers is having with the class and the problems.

FIG. 6 shows a virtual classroom with e material used as learning medium, based on study of eye movement, pupillary dilation and facial study. The material will be constantly modified and since all peers will be present constant discussion will also be taking place.

If it is observed that the user wasn't taking in the course then pop up questions will be presented on the work area, to check the users understanding hence allow for optimised learning.

Also, based on eye movement, pupillary dilation and facial study of other peers, the student can detect the amount of difficulty or ease his/her peers is having with the class and the problems. Areas that seem to be confusing for the student will be noted down and at the end of each study session these areas will be reviewed.

SWAP Projects

FIG. 7 shows an embodiment of a user interface. For example, another feature that will be present along with the education globe is a single sheet that can be shared across an entire group. All members of the group can make modifications to the sheet simultaneously. All editing and word processing features will be made available. This will allow for rapid completion of project with different parts (e.g. one person may be drawing up some part while others may be writing) being done by different people. In FIG. 7, for example, Linda, Jenny, and Robert can all see each other's videos. The “Toolbar” includes all possible software devices (e.g., tables, different languages, presentation tools, graphical tools, etc.) In this image, one of the users is able to see what he/she has entered into the project.

Since all progress being made is constantly visible to all the users working on it, a seamless integration will be possible. In fact different people can comment and suggest changes to some or more parts being done by someone else. Constant discussion and visibility amongst the different team members will also be facilitated through audio and videoconference, which will run in parallel with the SWAP Project feature. This will have huge utility in corporate sector, which generally have members working on single project scattered all over the globe.

FIG. 8 shows an embodiment of a collated version of the user interface. On a single screen, smaller images of various different types of software applications can be presented. Also each user's specific part is labelled automatically with their name. Thus, users are able to see the different segments of the project that are being completed by other users.

Targeted Advertisements

Advertisement will be presented to users based on

-   -   a. Keyword matching     -   b. Based on knowledge of user's real-time emotional state.     -   c. Geographic location and movement pattern (for people using         mobile access medium like cell phones or tablets)

The advertisements that will be presented will be guided based on the content of the conversation, the mood of the user and the feature that of SWAP that is being used.

For example people who show high level of cognitive stress may be suggested stress-relaxing medicine, names of specialists and people. People showing emotional extremes like extreme depression may be suggested holiday destinations and retreats, or books.

For mobile users the geographical location, path and movement pattern of the user will be taken into account to provide location based targeted advertisement where product that might appeal to user (predicted by taking into factors like nature of conversation, or media being observed, mood of the user and geographical position). This will enable advertisement agencies to provide extremely specific advertisement.

Healthcare (Remote Diagnosis)

Advanced application can be developed which will collect data generated from cell phones and transfer these to service provider who will analyse the data and transfer it to the healthcare agencies who can then provide diagnosis on basis of the data provided.

Advancement in cloud computing enables us to utilise same apps from different computing devices like tablets, computers, laptops and cell phones. The apps thus developed will not device or platform specific but will only be user specific, they will have an inventory of data mining mechanisms and depending on the device being used select mechanisms will be used.

Combination of data collected from the multiple sources will used to determine lifestyle of the person and this can be used by healthcare and insurance industries. This cycle is depicted in FIG. 9, which shows an embodiment of how insurance or health care companies will acquire date through the cell phone of a client. The cellular data will go through a telecom operator, through a 3^(rd) party service provider, who will collect and analyze the data and return it back to the company.

3rd party provider can collect this data only after approval from the individual who owns the cellular device over a set period of time. The data can be used by the individual for personal usage or along with his/her doctor for health analysis. For example, an individual who is fighting with obesity can have his/her cellular data tracked for one month. After analysis of this data, the doctor and patient (e.g., an obese individual) can work together to target some of the problems that the patient. On the other hand, health insurance companies can use this data after approval from the potential customer to determine how healthily he/she is living. If the behavioural choices, emotions, and other everyday actions of the customer seem to promote healthy lifestyle, the insurance company can give discounted rates to such a costumer. There are three methods by which current day smart phones can determine the lifestyle, behaviour, or emotions of a person. Time and location, the audio vector of the cellular device, and typing characteristics can be used to analyse a person's health. This data will be collected over a span of time.

Lifestyle data will include:

1. Location information

2. Driving information and movement information

3. His affective state and average cognitive state

4. Habitual information—diet, drinking, etc.

5. Real-time information about physical health

The span of time and monitoring parameters will be determined jointly by user and concerned agency.

1. Location Information:

The geographical location of a person can give a general idea of the person's life style and personality. Information like movement over different non-urban terrain is indicative of an adventurous lifestyle. Also information like the places the person visits will highlight many of the persons traits e.g., location data showing that one visits McDonald's everyday indicates that the individual does not have a healthy lifestyle, compared to an individual who visits the gym on a daily basis. After large enough samples of data are collected, a movement map of the individual can be created that shows frequencies of visits to certain locations within a certain range. Using a pattern identification algorithm, doctors or life insurance agencies can more easily analyse location data of an individual and correlate this to his/her lifestyle.

2. Driving Information and Movement Information:

Velocity and acceleration analysis can be done by the GPS on the phone to determine whether or not the individual is a rash driver. Information about speed limits on a majority of roads is present on the maps that are on smart phones. It can be understood that an individual is driving if they are on a road that is traversed upon by vehicles. Usually, GPS tracking provides an accuracy of within 50 meters. So, the speed of a person can be determined by dividing each 50-meter path covered by the time required by the individual to traverse that distance. It will be noted that a person is walking, not driving, on such a road if the speed is significantly below that of the speed limit (like below 10 km/s) for an extended period of time. Even this information is vital, as it informs that the individual is walking on a road that is meant for vehicles, which in itself is an unsafe behaviour. This behaviour will not be confused with cars that are just stuck in traffic, because traffic patterns are now being updated constantly to smart phones, and data about the location and time of the traffic can easily be collected. After confirming that the individual is driving on the road, one can compare the speed of his/her vehicle with the speed to determine whether or not the person is speeding. Even if the individual whose data is being taken down is not the driver, it is important to know if the individual is at risk by being in the same vehicle as a person who is speeding. In addition, if the average velocities recorded in each 50 meter block are fluctuating highly, and the time taken to cover one 50 meter stretch is significantly different than the time taken to cover another, one can see that the driving is “stopping and going” too frequently. An accumulation of data about velocity can easily be translated into acceleration analysis, where the rashness of the driver with sudden accelerations can be determined.

3. The Affective and Cognitive State:

The user emotional and cognitive data will have obtained from all communications taking place in form texting, video chat and audio chat from devices like smart phones, tablets computers or laptops. Since the functioning of various features of SWAP like profile+ and virtual classrooms is heavily of dependent on user emotion and cognitive state the apps can gather data from these features to observe emotional and cognitive states of the user during the period of observation. These data can be combined with location data (considering the fact that the user is constantly carrying his smart phone) to affect map of the person. The affect map will show which emotions and mental state correspond to specific locations of the individual.

4. Habitual information:

Various apps and detection mechanisms can be utilised to determine various habits of the user like eating habits, drinking habit, smoking habit, etc. Apps like MEALSAPP®, etc. can be detected by the advanced apps of SWAP and used to detect traits of the user.

5. Physical Health information:

Smart phones have pedometers installed in them and also have the capacity to find a person's pulse. All these features can be used by advanced SWAP apps to give a person's physical health status which can be further combined with time and location information supplement the above-mentioned data.

From this network, an emotional map can also be constructed that shows which emotions correspond to specific locations of the individual. This location tracking combined with the audio vector and typing analysis can indicate which locations the individual should continue going to boost happiness and which locations should be avoided, as they may be correlated to stress, anger, sorrow, etc.

Emotion Analysis

The large amount of data that will be passing through SWAP will be analysed in following ways:

1. Video Analysis

2. Speech Analysis

3. Typing analysis

FIG. 10 illustrates an embodiment of how the media acquired by SWAP is going to be analyzed. The data can be organized into three segments: Video, Text, and Audio. Pupillary dilation analysis and facial feature analysis can be taken from the video data analysis. From textual data, keywords, knowledge-based artificial neural networks, typing speed, pressure, contextual clue and error analyses can be done. From audio data, features can be extracted and analyzed. These can be used to determine emotion.

1. Video Analysis

-   -   a. Facial emotion recognition         -   The emotion of user is recognized by tracking the movements             of some fixed points of the face like the corners of eyes,             mouth boundary, etc. The amount of movement of these points             in various frames of the video are constantly monitored and             the data thus generated is fed in various classifiers like             Bayesian Networks, Decision Trees etc. from which we find             the emotion of the user.     -   b. Pupillary Dilation         -   Dilation of pupils is common phenomena. The causes for             dilation of pupils are:             -   1. Mental stress (cognitive load).             -   2. Emotion             -   3. Light stimulus

Our pupils tend to dilate in different emotional situation. Studies conducted have shown that with increase in arousal level the diameter of out pupils increase. Also valance causes our pupils to dilate. But the amount of dilation caused for positive and negative emotion has been found out to be the same. This issue may be resolved with further study in this area—analyzing the rate of dilation and dilation period and also the amount and rate of dilation under combination of different stimuli. Also while measuring pupil dilation, the dilation caused due other stimuli like light have 2 either ignored or factored out (more study is required in this area). Pupillary dilation is a complete involuntary reflex and hence there no change for us to consciously control it. (This is possible in case facial emotion recognition.) Hence no emotion faking is possible. A distinct difference is apparent for male and female users. So, gender classification can be done easily through study of pupil dilation pattern.

2. Speech Analysis

To find out emotion from speech the basic idea is to study the way the voice box functions while producing speech under different emotional states. Depending upon how it functions variations in wave form appear. By extracting the various features of the waveform from which these variations can be detected and putting these (certain combinations of features) into various soft computing models the emotion can be predicted.

Data extracted from an audio vector can be used to determine one's emotional state. The volume and pitch of the speaker can be found without actually recording what the speaker is saying, avoiding any invasion of privacy. The content of the conversation is immaterial to the 3^(rd) parties, since only the tonal nature (loudness and frequency) of the individual is being analyzed.

To find emotion from speech first we extract various components of speech, which carry data with respect to emotion. These components are energy, pitch, cross sectional area of vocal tract tube, formant, speech rate and spectrum features and spectral features like linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Mel frequency cepstrum coefficients (MFCCs) and its first derivative and log-frequency power coefficients (LFPCs). All these components are extracted from the original speech waveform using various mathematical and statistical techniques. The features can be extracted utilizing various combinations of the features. These acoustic features are used to find out emotions through various classifiers.

Methods that classify emotions from prosody contours are neural networks, multi-channel hidden Markov model, mixture of hidden Markov models these give prediction from the temporal information of speech

Methods which classify emotions from statics of prosody contours support vector machines, k-nearest neighbours, Bayes classifiers using pdf (probability distribution functions) generated by Parzen windows, Bayes classifier using one Gaussian pdf, Bayes classifier using mixture of Gaussian pdfs.

Hence from the above mentioned soft computing techniques we find the emotion of a person. From this his type of collection over a large span of time, general emotional status can be determined via the audio vector.

Data extracted from an audio vector can be used to determine one's emotional state. The volume and pitch of the speaker can be found without actually recording what the speaker is saying, avoiding any invasion of privacy. The content of the conversation is immaterial to the 3^(rd) parties, since only the tonal nature (loudness and frequency) of the individual is being analysed.

3. Typing Analysis

We will utilize the following methods to find emotion of the user from the text that he types. All the methods will be working in parallel.

-   -   1. Finding emotional keywords in textual data and deriving the         emotion of the sentence from that.     -   2. Finding emotion from sentences, lacking emotion key words         using Knowledge Based Artificial Neural Networks.     -   3. By analyzing the typing speed. The various features of typing         that we study are time lag between consecutive keystrokes     -   4. Error level. (Number of times corrections are made in the         sentences).     -   5. Pressure Analysis—the pressure sequence various features         extracted like mean, standard deviation, maximum and minimum         energy difference, the positive energy center (PEC) and the         negative energy center (NEC). PEC and NEC are calculated from         mean and standard deviation after normalization).     -   6. Contextual cue analysis weather, lighting, temperature,         humidity, noise level and shaking of the phone

The various features of typing that we study are time lag between consecutive keystrokes, number of times back space is used, typing speed and pressure put behind each keystroke, for example, from the pressure sequence various features extracted like mean, standard deviation, maximum and minimum energy difference, the positive energy centre (PEC) and the negative energy centre (NEC). PEC and NEC are calculated from mean and standard deviation after normalisation). Apart from these various contextual cues are also taken into account like weather, lighting, temperature, humidity, noise level and shaking of the phone, and the frequency of certain characters, words, or expressions can be used to determine emotion. The above mentioned sets of features are fed into various soft computing models (like support vector machines, Artificial neural networks, Bayesian networks, etc.), these generate probability towards a particular emotional state individually for each set of features. Also, since in most cases the outcome will be towards the same emotion from computations on each feature set hence fusion methods can be used to compute the over all probability of having that particular emotion by combining the individual results.

Towards Development of a Model for Emotion Detection from Typing Analysis

First we find out features of typing which is exhibited by most people and features of these patterns which detect emotions. We now develop various soft computing models which allow for the detection of a particular emotion from the typing pattern. To see the efficiency and functionality of these models we conduct sample studies where a software is downloaded by the people whose typing pattern will be analysed. Apart from the typing pattern detection another detection method will also be there to measure the emotional state at the time of typing. These 2 methods will work in parallel and the emotion detected by latter method will be taken as reference and later during analysis it will be seen whether the emotion predicted by the former method matches with the reference.

In the latter method the peoples' emotional valence will be detected by study of their facial muscles which can be done by use of a simple web-cam (generally available with their computer or laptop) and arousal will be detected by measuring the galvanic conductivity of skin measured with wristband with this capability (already a commercial product manufactured by a company called AFFECTIVA®).

The above-mentioned method departs away from way experiments have been done on typing analysis recently. In these experiments the candidates who's pattern will be analysed are given the software which analyses the typing pattern but reference emotion is found out through questionnaires that enquire about the emotion of the person before he starts to type.

Again, this will not be a privacy issue because these third parties will not access full texts. They will just automatically search through them for the frequency of specific words or expressions that may correlate to the individual's emotions. These data will not just be collected once, but over a long span of time. As a result, the overall emotional and behavioural state of individual will be determined. So, a person typing very fast on a shaking phone, with high pressure under the keys, and using a high frequency of unpleasant words used in his/her texts can reveal anger or stress. However, if data that points to this behaviour is only collected once or twice in a span of a month, it will not be regarded as very important, as everyone has some infrequent expressions of anger or stress. However, if a majority of typing data is like this, a doctor of insure company can infer that the individual is constantly angry or stressed out, which is not good for health.

Mental Health Tracker

Currently 1 in 4 Americans have a mental disorder. It is becoming increasingly important to identify mental disorders at younger age, when symptoms are still slight. It is thus essential for primary care physicians in addition to psychiatrists to be able to recognize mental disorders.

In an embodiment, the DSM IV-TR (Diagnostic and Statistical Manual for Mental Disorders) and DSM IV-PC (Diagnostic and Statistical Manual for Primary Care) version, which are the manuals used by doctors to determine both the presence and category of mental disorder, could be included in as part of a computerized algorithm to help doctors for patient tracking. The DSM IV-PC (meant for primary care physicians, who are not specialized in mental disorders) has organized symptoms that create a diagnostic algorithm. This manual is concise and fully compatible with the wider used DSM IV-TR, which is used by psychiatrics.

Primary care physicians (PCP) have made many initial diagnoses of mental disorders. However, many diagnoses remain undetected, as PCPs generally only have check-ups with patients one or twice a year, and mental disorders, at first may be difficult to observe, as there are no standardized tests for mental disorders. Due to the difficulty in diagnosing a mental disorder within the limited face-to-face patient-doctor interaction, it can be extremely helpful for doctors to use a non-invasive patient tracking method of an embodiment as shown in FIG. 11, which shows the main aspects of SWAP that can be used to create a profile of the patient that can then be analyzed by the algorithm of the DSM-IV and by doctors.

Doctors can track their patients using methods detailed in other examples of our patent. FIG. 12 shows a flow diagram that delineates possible tracking mechanisms. FIG. 12 shows that the proposed tracker can use video data, time and location analysis, typing analysis, and audio data in order to understand the patient's emotional state. Over a week or month long analysis, this tracker will then use an algorithm from the DSM-IV in order to identify an initial mental diagnosis. With the use of the guidelines in the DSM-IV-PC, the algorithms created by the manual can be used along with our tracking system to provide a primary initial screening for patients for detection and type of mental disorder. Thus, SWAP's Mental Health Tracker can help a physician better understand his patient's needs.

APPENDIX 1

The U.S. patents and publications listed below are hereby incorporated herein by reference in their entirety. U.S. Pat. No. 8,102,406; Issue date: Jan. 24, 2102; Method and system for producing a video synopsis U.S. Pat. No. 8,073,839; Issue date: Dec. 6, 2011; System and method of peer to peer searching, sharing, social networking and communication in one or more networks U.S. Pat. No. 7,523,163; Issue date: Apr. 21, 2009; Distributed network system architecture for collaborative computing U.S. Pat. No. 7,313,595; Issue date: Dec. 25 2007; System and method for record and playback of collaborative web browsing session U.S. Pat. No. 7,236,926; Issue date: Jun. 26, 2007; System and method for voice transmission over network protocols U.S. Pat. No. 6,567,813; Issue date: May 20, 2003; Quality of service maintenance for distributed collaborative computing Publication number: US 2011/0258125; Filing date: Apr. 14, 2011; Collaborative social event planning and execution Publication number: US 2011/0225519; Filing date: Feb. 16, 2011 Social media platform for simulating a live experience Publication number: US 2011/0066664; Filing date: Sep. 15, 2010; Sports collaboration and communication platform Publication number: US 2010/0299334; Filing date: Sep. 8, 2009; Computer implemented system and method for providing a community and collaboration platform around knowledge transfer, expertise, innovation, tangible assets, intangible assets and information assets Publication number: US 2010/0332616; Filing date: Aug. 31, 2009; Web guide Publication number: US 2010/0262550; Filing date: Apr. 8, 2009; Inter-corporate collaboration overlay solution for professional social networks Publication number: US 2009/0094039; Filing date: Oct. 4, 2007; Collaborative production of rich media content Publication number: US 2008/0297588; Filing date: May 31, 2007, Managing scene transitions for video communication Publication number: US 2005/0198141; Filing date: Feb. 4, 2005; Secure communications system for collaborative computing Publication number: US 2003/0167304; Filing date: Dec. 29, 2000; Distributed meeting management Publication number: US 2003/0164853; Filing date: Dec. 29, 2000; Distributed document sharing 

What is claimed is:
 1. A method of establishing a collaborative platform comprising: performing a collaborative interactive session among a plurality of members, analyzing affect and cognitive features of the plurality of members by obtaining video images from each of the plurality of members, wherein video images comprise pupil dilation, eye tracking and/or facial features of each of the plurality of the members, analyzing in real-time the video images using a video analysis system, presenting questions to each of the plurality of the members during a sub-segment of the collaborative interactive session, and starting a next sub-segment of the collaborative interactive session only after a minimum number of the questions are completed by the plurality of the members.
 2. The method of claim 1, wherein the plurality of members from different human interaction platforms interact via the collaborative platform,
 3. The method of claim 1, further comprising displaying of targeted advertisements or notifications based on the context of the interactive collaborative session.
 4. The method of claim 3, further comprising measuring effectiveness of the displaying of targeted advertisements or notifications.
 5. The method of claim 1, further comprising integrating an application or a device within the collaborative interactive session.
 6. A computer implemented system comprising: a storage medium configured to store data from the collaborative platform of claim 1; and a processor configured to perform a collaborative interactive session for a plurality of members, wherein the system analyses affect and cognitive features of the plurality of members.
 7. The system of claim 6, wherein the plurality of members from different human interaction platforms interact via the collaborative interactive session, wherein the different human interactions platforms comprise social media platforms.
 8. The system of claim 6, wherein the system is further configured to display targeted advertisements or notifications based on the context of the interactive collaborative sessions.
 9. The system of claim 8, wherein the system is further configured to measure effectiveness of the displaying of targeted advertisements or notifications.
 10. The system of claim 6, wherein the system is further configured to integrate an application or a device within the collaborative interactive session.
 11. The system of claim 6, wherein the system comprises a sound and/or video hub, wherein the sound and/or video hub allows any member of the plurality of the members to play a song and/or a video and simultaneously allows the plurality of members to listen and/or watch the song and/or the video played.
 12. The system of claim 6, wherein the system comprises audio and/or video synopsis of the collaborative interactive session for the plurality of members using a sound and image-processing technology that creates a summary of an original full-length audio and/or video.
 13. The system of claim 6, wherein system is configured to determine a mental health of a patient by analyzing one or more of audio, video, textual and location data of the patient, and evaluating the data in a standardized model.
 14. A tangible non-transitory computer readable medium comprising computer executable instructions executable by one or more processors for establishing the collaborative platform of claim 1, comprising performing a collaborative interactive session for a plurality of members, and analyzing affect and cognitive features of the plurality of members.
 15. The medium of claim 14, wherein the plurality of members interacts from different human interaction platforms.
 16. The medium of claim 14, further comprising computer executable instructions executable by one or more processors for displaying of targeted advertisements or notifications based on the context of the interactive collaborative sessions.
 17. The medium claim 14, wherein the executable instructions comprise instruction for determining a mental health of a patient by analyzing one or more of audio, video, textual and location data of the patient, and evaluating the data in a standardized model. 